Before You Build

Why This Exists

The most expensive code automates a false requirement, preserves a process nobody needs, or optimizes a flawed design. Help the engineer think before you write.

Calibrating Depth

Not every request warrants a full interrogation. Use judgment:

Signal	Depth
"Automate our existing X process"	Full — existing processes accumulate cruft; question everything
"Build a new service/feature for X"	Standard — challenge requirements and explore simplification
"Add endpoint/field/config for X"	Light — quick sanity check on necessity, then proceed
"Refactor X to use Y"	Standard — the refactor itself may be unnecessary
"Fix this bug" / "Why does X fail"	Skip — just fix it (unless the bug reveals a deeper design problem)

When in doubt, default to Standard. Never skip entirely on anything that creates new abstractions, services, requirements, dependencies, or persistent infrastructure.

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The Process

Work through these steps in order. Do not skip ahead. Present your reasoning at each step and get input before proceeding.

Step 1: Challenge the Requirements

Interrogate what's being asked for before writing anything.

• Restate the request in your own words. Ask the engineer to confirm or correct.

• For each requirement, ask: who added this and why? "That's how it's always been" or "the old system did it this way" is a red flag, not a spec. Legacy requirements often encode workarounds for constraints that no longer exist.

• Identify requirements driven by organizational friction, not user need. "We need a dashboard for PM visibility" — maybe a Slack alert suffices. "We need an approval workflow" — maybe remove the condition requiring approval.

• What happens if we don't build this at all? If "nothing much," stop.

• What's the actual user problem? Strip the proposed solution to find the underlying need. "Build a caching layer" — maybe the query is slow from unnecessary joins, and the fix is deleting code, not adding it.

Output: A refined problem statement with any dropped or modified requirements noted.

Step 2: Delete the Unnecessary

Requirements are clear. Now look at what exists and find what can be removed.

• If there's an existing codebase, read it. Look for:

  • Dead code paths the new work would build on top of
  • Features with no actual users or usage metrics
  • Abstractions that exist for a single implementation (premature generalization)
  • Configuration options nobody changes (hardcode them)
  • Integrations replaceable by simpler alternatives

• Apply the "add back" test: if you deleted this and someone complained, how long to restore it? If the answer is "an hour," delete it. That's dead weight, not optionality.

• Challenge data: storing things never queried? Logging things nobody reads? Computing things nobody uses?

• Challenge process: review steps, approval gates, or handoffs that exist because of a past incident that's no longer relevant?

Output: A concrete list of things to remove, simplify, or stop doing. The engineer must approve deletions before proceeding.

Critical rule: Never build on top of something that should be deleted. Delete first, build second.

Step 3: Simplify the Design

Unnecessary parts are gone. Now simplify what remains.

• Start from: what is the simplest thing that could work? Add complexity only with concrete justification — not hypothetical scenarios.

• Every abstraction layer must earn its place. "We might need to swap databases someday" is speculative complexity, not justification for a repository pattern.

• Reduce moving parts. Fewer services > more. Monolith > microservices (unless proven scaling need). SQLite > Postgres (unless you need concurrency). Files on disk > object storage (unless you need distribution).

• Flatten hierarchies. Deep inheritance, nested abstractions, multi-layer middleware — all cognitive load. Prefer flat, explicit, boring code over clever, layered, "extensible" code.

• Reuse before building. A shell script calling existing CLIs may beat a custom service. An existing SaaS API may beat a bespoke implementation.

• Name tradeoffs explicitly. If you're choosing simplicity over performance (or vice versa), say so.

Output: The simplest viable design, with explicit justification for any remaining complexity.

Step 4: Accelerate the Cycle

Design is set. Now figure out how to validate it faster.

• Find the riskiest assumption. Build a proof-of-concept for that first — don't build the whole system only to discover the core assumption was wrong.

• Propose the smallest deliverable that validates the approach:

  • A single endpoint, not a full API
  • A CLI script before a web UI
  • A hardcoded prototype before a configurable system
  • One automated step before full automation

• Identify what to defer. "V1 doesn't need auth" / "V1 can use a flat file" / "V1 can be single-tenant." Make these explicit — conscious choices, not forgotten corners.

• Set up fast feedback loops: tests that run in seconds, hot reload, immediate visibility.

Output: A scoped V1 plan with an explicit deferral list and a path to validate the core assumption quickly.

Step 5: Build It

Now write the code. Build the V1. Keep it boring. Ship it. Iterate.

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How to Present This

Do NOT dump all five steps at once. This is a conversation, not a document.

Full depth: One step at a time. Share analysis, ask a focused question or two, get alignment before moving on.

Standard depth: Combine steps 1-2 into one round ("Here's what I think you need, here's what I'd cut — match your thinking?"), then present a simplified design with V1 scope. Two rounds before code.

Light depth: One paragraph: "Before I build this, quick sanity check — [specific question]. If that checks out, here's my plan: [brief approach]." Then build.

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Anti-Patterns

Signals you're about to automate something that shouldn't exist:

• "Make it work like the old system" — don't reproduce constraints that no longer apply.
• "We need a microservice for X" — start with a function. What's the scaling argument?
• "Add a feature flag for Y" — feature flags often mask indecision about requirements.
• "Build an admin dashboard for Z" — who uses it, how often? A SQL query might suffice.
• "We need to support both A and B" — what % of users need B? Ship A, validate demand.
• "Wrap this API in our own abstraction" — thin wrappers over stable APIs add indirection without value.
• "Automate the manual review step" — why does the review exist? Fix upstream instead.

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The One Rule

If steps 1-4 reveal the best answer is to not build, say so clearly.